Emergency Department (ED) is a complex care delivery environment in a hospital that provides time sensitive urgent and lifesaving care [1]. Emergency medicine is an unscheduled practice and therefore providers experience extreme fluctuations in their workload. ED crowding is a major concern that affects the efficacy of the ED workflow, which often is challenged by long wait times, overuse of observation units, patients either leaving without being seen by a provider and non-availability of inpatient beds to accommodate patients after diagnosis [2]. Evaluating ED workflow is a challenging task due to its chaotic nature, with some success using time-motion studies and novel capacity management tools are nowadays becoming common in ED to address workflow related issues [3]. Several studies reveal that Electronic Medical Record (EMR) adoption has not resulted in significant ED workflow improvements nor reduced the cost of ED operations. Since raw EMR data does not offer operational and clinical decision making insights, advanced EMR data analytics are often sought to derive actionable intelligence from EMR data that can provide insights to improve ED workflow. Improving ED workflow has been an important topic of research because of its great potential to optimize the urgent care needed for the patients and at the same time save time and cost.
Radio Frequency Identification Device (RFID) is a wireless automatic identification and data capture technology device that has the potential for improving safety, preventing errors, saving costs, and increasing security and therefore improving overall organizational performance. RFID technology use in healthcare has opened a new space in healthcare informatics research that provides novel data to identify workflow process pitfalls and provide new directions [4]. The potential advantages of RFID adoption in healthcare and especially in ED has been well recognized to save costs and improve care delivery [5]. However, the large upfront infrastructure costs, need for an integrated health information technology (HIT), advanced analytical tools for big data analysis emerging from RFID and skilled data scientists to tackle the data to derive actionable intelligence discourage many hospitals from adoption RFID technology despite its potential advantages.
Our recent pilot study on the RFID data analytics demonstrated the feasibility of quantifying and analyzing two novel variables such as ‘patient alone’ time defined as the total time a patient spends alone without interaction with a health care staff in the ED and ‘provider time’ defined as the total time a patient spends interacting with any health care staff [6]. The study motivated a more comprehensive big data analytics of RFID data which can provide better insights into optimizing ED workflow which can improve the quality of care in the ED and also reduce cost. In this work, the authors attempt to describe the RFID adoption in the ED at the Saint Mary’s Hospital at Mayo Clinic, in Rochester, MN, a level one trauma center both for children and adults as a step towards optimizing ED workflow.
An RFID Data Matrices-Based Evaluation Method for Process Logistics State
